Deductions from a Sub-Saharan African bank’s tweets : a sentiment analysis approach

The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Sahar...
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Autor*in:	Botchway, Raphael Kwaku [verfasserIn] Jibril, Abdul Bashiru [verfasserIn] Oplatková, Zuzana Komínková [verfasserIn] Chovancová, Miloslava [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	social media twitter tweet sentiment analysis VADER Ecobank Africa

Übergeordnetes Werk:	Enthalten in: Cogent economics & finance - Abingdon : Taylor & Francis, 2014, 8(2020), 1, Seite 1-19
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:1 ; pages:1-19

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/23322039.2020.1776006

Katalog-ID:	1751082229

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allfields	10.1080/23322039.2020.1776006 doi 10419/245324 hdl (DE-627)1751082229 (DE-599)KXP1751082229 DE-627 ger DE-627 rda eng Botchway, Raphael Kwaku verfasserin (DE-588)1229228357 (DE-627)1751200477 aut Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206 Jibril, Abdul Bashiru verfasserin (DE-588)1198901705 (DE-627)1681084317 aut Oplatková, Zuzana Komínková verfasserin (DE-588)1229228721 (DE-627)1751200728 aut Chovancová, Miloslava verfasserin (DE-588)1220056901 (DE-627)1736252348 aut Enthalten in Cogent economics & finance Abingdon : Taylor & Francis, 2014 8(2020), 1, Seite 1-19 Online-Ressource (DE-627)786946385 (DE-600)2773198-4 (DE-576)407862285 2332-2039 nnns volume:8 year:2020 number:1 pages:1-19 https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true Verlag kostenfrei https://doi.org/10.1080/23322039.2020.1776006 Resolving-System kostenfrei http://hdl.handle.net/10419/245324 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 1 1-19 26 01 0206 3884694871 x1z 11-03-21 2403 01 DE-LFER 3906604993 00 --%%-- --%%-- n --%%-- l01 12-04-21 2403 01 DE-LFER https://doi.org/10.1080/23322039.2020.1776006 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true
spelling	10.1080/23322039.2020.1776006 doi 10419/245324 hdl (DE-627)1751082229 (DE-599)KXP1751082229 DE-627 ger DE-627 rda eng Botchway, Raphael Kwaku verfasserin (DE-588)1229228357 (DE-627)1751200477 aut Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206 Jibril, Abdul Bashiru verfasserin (DE-588)1198901705 (DE-627)1681084317 aut Oplatková, Zuzana Komínková verfasserin (DE-588)1229228721 (DE-627)1751200728 aut Chovancová, Miloslava verfasserin (DE-588)1220056901 (DE-627)1736252348 aut Enthalten in Cogent economics & finance Abingdon : Taylor & Francis, 2014 8(2020), 1, Seite 1-19 Online-Ressource (DE-627)786946385 (DE-600)2773198-4 (DE-576)407862285 2332-2039 nnns volume:8 year:2020 number:1 pages:1-19 https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true Verlag kostenfrei https://doi.org/10.1080/23322039.2020.1776006 Resolving-System kostenfrei http://hdl.handle.net/10419/245324 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 1 1-19 26 01 0206 3884694871 x1z 11-03-21 2403 01 DE-LFER 3906604993 00 --%%-- --%%-- n --%%-- l01 12-04-21 2403 01 DE-LFER https://doi.org/10.1080/23322039.2020.1776006 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true
allfields_unstemmed	10.1080/23322039.2020.1776006 doi 10419/245324 hdl (DE-627)1751082229 (DE-599)KXP1751082229 DE-627 ger DE-627 rda eng Botchway, Raphael Kwaku verfasserin (DE-588)1229228357 (DE-627)1751200477 aut Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206 Jibril, Abdul Bashiru verfasserin (DE-588)1198901705 (DE-627)1681084317 aut Oplatková, Zuzana Komínková verfasserin (DE-588)1229228721 (DE-627)1751200728 aut Chovancová, Miloslava verfasserin (DE-588)1220056901 (DE-627)1736252348 aut Enthalten in Cogent economics & finance Abingdon : Taylor & Francis, 2014 8(2020), 1, Seite 1-19 Online-Ressource (DE-627)786946385 (DE-600)2773198-4 (DE-576)407862285 2332-2039 nnns volume:8 year:2020 number:1 pages:1-19 https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true Verlag kostenfrei https://doi.org/10.1080/23322039.2020.1776006 Resolving-System kostenfrei http://hdl.handle.net/10419/245324 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 1 1-19 26 01 0206 3884694871 x1z 11-03-21 2403 01 DE-LFER 3906604993 00 --%%-- --%%-- n --%%-- l01 12-04-21 2403 01 DE-LFER https://doi.org/10.1080/23322039.2020.1776006 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true
allfieldsGer	10.1080/23322039.2020.1776006 doi 10419/245324 hdl (DE-627)1751082229 (DE-599)KXP1751082229 DE-627 ger DE-627 rda eng Botchway, Raphael Kwaku verfasserin (DE-588)1229228357 (DE-627)1751200477 aut Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206 Jibril, Abdul Bashiru verfasserin (DE-588)1198901705 (DE-627)1681084317 aut Oplatková, Zuzana Komínková verfasserin (DE-588)1229228721 (DE-627)1751200728 aut Chovancová, Miloslava verfasserin (DE-588)1220056901 (DE-627)1736252348 aut Enthalten in Cogent economics & finance Abingdon : Taylor & Francis, 2014 8(2020), 1, Seite 1-19 Online-Ressource (DE-627)786946385 (DE-600)2773198-4 (DE-576)407862285 2332-2039 nnns volume:8 year:2020 number:1 pages:1-19 https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true Verlag kostenfrei https://doi.org/10.1080/23322039.2020.1776006 Resolving-System kostenfrei http://hdl.handle.net/10419/245324 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 1 1-19 26 01 0206 3884694871 x1z 11-03-21 2403 01 DE-LFER 3906604993 00 --%%-- --%%-- n --%%-- l01 12-04-21 2403 01 DE-LFER https://doi.org/10.1080/23322039.2020.1776006 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true
allfieldsSound	10.1080/23322039.2020.1776006 doi 10419/245324 hdl (DE-627)1751082229 (DE-599)KXP1751082229 DE-627 ger DE-627 rda eng Botchway, Raphael Kwaku verfasserin (DE-588)1229228357 (DE-627)1751200477 aut Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206 Jibril, Abdul Bashiru verfasserin (DE-588)1198901705 (DE-627)1681084317 aut Oplatková, Zuzana Komínková verfasserin (DE-588)1229228721 (DE-627)1751200728 aut Chovancová, Miloslava verfasserin (DE-588)1220056901 (DE-627)1736252348 aut Enthalten in Cogent economics & finance Abingdon : Taylor & Francis, 2014 8(2020), 1, Seite 1-19 Online-Ressource (DE-627)786946385 (DE-600)2773198-4 (DE-576)407862285 2332-2039 nnns volume:8 year:2020 number:1 pages:1-19 https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true Verlag kostenfrei https://doi.org/10.1080/23322039.2020.1776006 Resolving-System kostenfrei http://hdl.handle.net/10419/245324 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2020 1 1-19 26 01 0206 3884694871 x1z 11-03-21 2403 01 DE-LFER 3906604993 00 --%%-- --%%-- n --%%-- l01 12-04-21 2403 01 DE-LFER https://doi.org/10.1080/23322039.2020.1776006 2403 01 DE-LFER https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true
language	English
source	Enthalten in Cogent economics & finance 8(2020), 1, Seite 1-19 volume:8 year:2020 number:1 pages:1-19
sourceStr	Enthalten in Cogent economics & finance 8(2020), 1, Seite 1-19 volume:8 year:2020 number:1 pages:1-19
format_phy_str_mv	Article
building	26:1 2403:0
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	social media twitter tweet sentiment analysis VADER Ecobank Africa
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container_title	Cogent economics & finance
authorswithroles_txt_mv	Botchway, Raphael Kwaku @@aut@@ Jibril, Abdul Bashiru @@aut@@ Oplatková, Zuzana Komínková @@aut@@ Chovancová, Miloslava @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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topic_title	Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová social media (dpeaa)DE-206 twitter (dpeaa)DE-206 tweet (dpeaa)DE-206 sentiment analysis (dpeaa)DE-206 VADER (dpeaa)DE-206 Ecobank (dpeaa)DE-206 Africa (dpeaa)DE-206
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title	Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach
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title_full	Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová
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title_sort	deductions from a sub-saharan african bank’s tweetsa sentiment analysis approach
title_auth	Deductions from a Sub-Saharan African bank’s tweets a sentiment analysis approach
abstract	The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper.
abstractGer	The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper.
abstract_unstemmed	The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. Limitations and future research directions are discussed at the end of the paper.
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title_short	Deductions from a Sub-Saharan African bank’s tweets
url	https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1776006?needAccess=true https://doi.org/10.1080/23322039.2020.1776006 http://hdl.handle.net/10419/245324
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up_date	2024-07-04T10:13:58.933Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1751082229</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20211130160218.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210311s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/23322039.2020.1776006</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/245324</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1751082229</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1751082229</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Botchway, Raphael Kwaku</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1229228357</subfield><subfield code="0">(DE-627)1751200477</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deductions from a Sub-Saharan African bank’s tweets</subfield><subfield code="b">a sentiment analysis approach</subfield><subfield code="c">Raphael Kwaku Botchway , Abdul Bashiru Jibril , Zuzana Komínková Oplatková and Miloslava Chovancová</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The upsurge in social media websites has in no doubt triggered a huge source of data for mining interesting expressions on a variety of subjects. These expressions on social media websites empower firms and individuals to discover varied interpretations regarding the opinions expressed. In Sub-Saharan Africa, financial institutions are making the needed technological investments required to remain competitive in today’s challenging global business environment. Twitter as one of the digital communication tools has in recent times been integrated into the marketing communication tools of banks to augment the free flow of information. In this light, the purpose of the present study is to perform a sentiment analysis on a large dataset of tweets associated with the Ecobank Group, a prominent pan-African bank in sub-Saharan Africa using four different sentiment lexicons to determine the best lexicon based on its performance. Our results show that Valence Aware Dictionary and sEntiment Reasoner (VADER) outperforms all the other three lexicons based on accuracy and computational efficiency. Additionally, we generated a word cloud to visually examine the terms in the positive and negative sentiment categories based on VADER. Our approach demonstrates that in today’s world of empowered customers, firms need to focus on customer engagement to enhance customer experience via social media channels (e.g., Twitter) since the meaning of competitive advantage has shifted from purely competing over price and product to building loyalty and trust. In theory, the study contributes to broadening the scope of online banking given the interplay of consumer sentiments via the social media channel. 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Deductions from a Sub-Saharan African bank’s tweets : a sentiment analysis approach

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